Various fixes, as suggested by Beni Altmann (CRAN).

asgr · Jun 6, 2024 · 64a31af · 64a31af
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,10 +1,10 @@
 Package: hyper.fit
 Type: Package
-Title: Generic N-Dimensional Hyperplane Fitting with Heteroscedastic Covariant Errors and Intrinsic Scatter
-Version: 1.1.3
-Date: 2020-10-06
-Author: Aaron Robotham and Danail Obreschkow
+Title: N-Dimensional Hyperplane Fitting with Heteroscedastic Errors
+Version: 1.2.0
+Date: 2024-06-06
+Author: c(person("Aaron", "Robotham", role = c("cre", "aut","cph"), email = "[email protected]"), person("Danail", "Obreschkow", role = c("aut", "ctb")))
 Maintainer: Aaron Robotham <[email protected]>
-Description: Includes two main high level codes for hyperplane fitting (hyper.fit) and visualising (hyper.plot2d / hyper.plot3d). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.
+Description: High level functions for hyperplane fitting (hyper.fit()) and visualising (hyper.plot2d() / hyper.plot3d()). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.
 License: GPL-3
 Depends: R (>= 3.00), magicaxis, MASS, rgl, LaplacesDemon
diff --git a/R/hyper.fit.R b/R/hyper.fit.R
@@ -47,18 +47,18 @@ hyper.fit=function(X,covarray,vars,parm,parm.coord,parm.beta,parm.scat,parm.erro
   }
 
   if(missing(parm) & missing(parm.coord) & missing(parm.beta) & missing(parm.scat)){
-    makeformula=function(objname='X',dims,vert.axis,env=.GlobalEnv){
+    makeformula=function(objname='X',dims,vert.axis){
       usedims=(1:dims)[-vert.axis]
       text=paste(objname,'[,',vert.axis,']~',paste(paste(objname,'[,',usedims,']',sep=''),collapse ='+'),sep='')
-      return=list(text=text,form=as.formula(text,env=env))
+      return=list(text=text,form=as.formula(text))
     }
     if(any(covarray[vert.axis,vert.axis,]>0)){
       startweights=1/sqrt(covarray[vert.axis,vert.axis,])
       startweights[is.infinite(startweights)]=max(startweights[is.finite(startweights)])
     }else{
       startweights=rep(1,N)
     }
-    startfit=lm(makeformula('X',dims=dims,vert.axis=vert.axis,env=environment())$form,weights=startweights*weights)
+    startfit=lm(makeformula('X',dims=dims,vert.axis=vert.axis)$form,weights=startweights*weights)
     startfit$coef[is.na(startfit$coef)]=0
     start.alphas=startfit$coef[2:dims]
     start.beta.vert=startfit$coef[1]

diff --git a/inst/CITATION b/inst/CITATION
@@ -10,4 +10,5 @@ Data with Multivariate Gaussian Uncertainties",
          journal = "PASA",
          volume = "32",
          pages = "33",
-         url = "http://hyperfit.icrar.org/")
+         url = "https://hyperfit.icrar.org/")
+
diff --git a/man/hyper.basic.Rd b/man/hyper.basic.Rd
@@ -175,7 +175,7 @@ Robotham, A.S.G., & Obreschkow, D., PASA, in press
 Aaron Robotham and Danail Obreschkow
 }
 \seealso{
-  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
+  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.fit-data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
 }
 \examples{
 extheta=30    #Experiment with changing this
@@ -247,9 +247,7 @@ print(arrayvecmult(array(1:24,c(2,3,4)),1:3))
 
 #Note that the following is not allowed:
 
-\dontrun{
-print(arrayvecmult(array(1:24,c(3,2,4)),1:3))
-}
+try(arrayvecmult(array(1:24,c(3,2,4)),1:3))
 
 }
 % Add one or more standard keywords, see file 'KEYWORDS' in the

diff --git a/man/hyper.convert.Rd b/man/hyper.convert.Rd
@@ -110,7 +110,7 @@ Robotham, A.S.G., & Obreschkow, D., PASA, in press
 Aaron Robotham and Danail Obreschkow
 }
 \seealso{
-  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
+  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.fit-data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
 }
 \examples{
 

diff --git a/man/hyper.data.Rd → man/hyper.fit-data.Rd b/man/hyper.data.Rd → man/hyper.fit-data.Rd
@@ -1,5 +1,5 @@
-\name{hyper.data}
-\alias{hyper.data}
+\name{hyper.fit-data}
+\alias{hyper.fit-data}
 \alias{hogg}
 \alias{intrin}
 \alias{trumpet}
@@ -67,7 +67,7 @@ Obreschkow & Glazebrook, 2014, ApJ, 784, 26
 Aaron Robotham and Danail Obreschkow
 }
 \seealso{
-  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
+  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.fit-data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
 }
 \examples{
 hogg=read.table(system.file('data/hogg.tab', package='hyper.fit'),header=TRUE)

diff --git a/man/hyper.fit-package.Rd b/man/hyper.fit-package.Rd
@@ -11,8 +11,8 @@
 \tabular{ll}{
 Package: \tab hyper.fit\cr
 Type: \tab Package\cr
-Version: \tab 1.1.3\cr
-Date: \tab 2020-10-06\cr
+Version: \tab 1.2.0\cr
+Date: \tab 2024-06-06\cr
 License: \tab GPL-3\cr
 }
 Most users will interact with this package through the high level hyper.fit and hyper.plot functions. The utility functions to construct covariance matrices and arrays will also be useful (see makecovmat2d and makecovarray2d).
@@ -26,7 +26,7 @@ Maintainer: Aaron Robotham <[email protected]>
 Robotham, A.S.G., & Obreschkow, D., PASA, 2015, 32, 33
 }
 \seealso{
-  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
+  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.fit-data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
 }
 \keyword{ package }
 \keyword{ fit }

diff --git a/man/hyper.fit.Rd b/man/hyper.fit.Rd
@@ -145,7 +145,7 @@ Robotham, A.S.G., & Obreschkow, D., PASA, in press
 Aaron Robotham and Danail Obreschkow
 }
 \seealso{
-  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
+  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.fit-data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
 }
 \examples{
 #### A very simple 2D example ####
@@ -215,7 +215,7 @@ plot(fitwitherrorandcor)
 
 #Setup the initial data:
 
-\dontrun{
+\donttest{
 
 set.seed(650)
 sampN=200
@@ -281,7 +281,7 @@ plot(fitline, parm.errorscale=fitlineerrscale$parm['errorscale'], trans=0.2, asp
 
 #Setup the initial data:
 
-\dontrun{
+\donttest{
 
 set.seed(650)
 
@@ -359,7 +359,7 @@ plot(fitlinenoexp, trans=0.2, asp=1)
 
 #Setup the initial data:
 
-\dontrun{
+\donttest{
 
 set.seed(650)
 sampN=200
@@ -427,7 +427,7 @@ plot(fitplane)
 
 #Load data:
 
-\dontrun{
+\donttest{
 
 data(hogg)
 
@@ -469,7 +469,7 @@ abline(v=quantile(fithoggtrimMCMC$fit$Posterior2[,3], c(0.95,0.99)), lty=2)
 
 #### Example using 'real' data with intrinsic scatter ####
 
-\dontrun{
+\donttest{
 
 data(intrin)
 
@@ -487,7 +487,7 @@ plot(fitintrincor, trans=0.1, pch='.', asp=1)
 
 #### Example using flaring trumpet data ####
 
-\dontrun{
+\donttest{
 
 data(trumpet)
 fittrumpet=hyper.fit(X=cbind(trumpet$x, trumpet$y), covarray=makecovarray2d(trumpet$x_err,
@@ -500,14 +500,17 @@ plot(fittrumpet, trans=0.1, pch='.', asp=1)
 
 }
 
-\dontrun{
+\donttest{
 
 #To find the likelihood of zero intrinsic scatter we will need to run LaplacesDemon. The
-#following will take a couple of minutes to run:
+#following will take a minute or so to run. Note we change the Specs for the
+#default Griddy Gibbs sampler.
 
 set.seed(650)
-fittrumpetMCMC=hyper.fit(X=cbind(trumpet$x, trumpet$y), covarray=makecovarray2d(trumpet$x_err,
-trumpet$y_err, trumpet$corxy), coord.type='normvec', algo.func='LD', itermax=1e5)
+fittrumpetMCMC = hyper.fit(X=cbind(trumpet$x, trumpet$y), covarray=makecovarray2d(
+  trumpet$x_err, trumpet$y_err, trumpet$corxy), coord.type='normvec', algo.func='LD',
+  itermax=1e4, Specs = list(Grid=seq(-0.001,0.001, len=5), dparm=NULL, CPUs=1,
+  Packages=NULL, Dyn.libs=NULL))
 
 #Assuming the user has specified the same initial seed we should find that the data
 #has exactly zero intrinsic scatter with ~47\% likelihood:
@@ -519,7 +522,7 @@ print(fittrumpetMCMC$zeroscatprob)
 
 set.seed(650)
 fittrumpetMCMCerrscale=hyper.fit(X=cbind(trumpet$x, trumpet$y), covarray=makecovarray2d(
-trumpet$x_err, trumpet$y_err, trumpet$corxy), itermax=1e5, coord.type='normvec', algo.func='LD',
+trumpet$x_err, trumpet$y_err, trumpet$corxy), itermax=1e4, coord.type='normvec', algo.func='LD',
 algo.method='CHARM', Specs=list(alpha.star = 0.44), doerrorscale=TRUE)
 
 #Assuming the user has specified the same initial seed we should find that the data
@@ -531,7 +534,7 @@ print(fittrumpetMCMCerrscale$zeroscatprob)
 
 #### Example using 6dFGS Fundamental Plane data ####
 
-\dontrun{
+\donttest{
 
 data(FP6dFGS)
 
@@ -547,7 +550,7 @@ plot(fitFP6dFGS, doellipse=FALSE, alpha=0.5)
 
 #Next we add the censoring weights provided by C. Magoulas:
 
-\dontrun{
+\donttest{
 
 fitFP6dFGSw=hyper.fit(FP6dFGS[,c('logIe_J', 'logsigma', 'logRe_J')],
 vars=FP6dFGS[,c('logIe_J_err', 'logsigma_err', 'logRe_J_err')]^2, weights=FP6dFGS[,'weights'],
@@ -565,7 +568,7 @@ in.coord.type='alpha'))
 
 #### Example using GAMA mass-size relation data ####
 
-\dontrun{
+\donttest{
 
 data(GAMAsmVsize)
 fitGAMAsmVsize=hyper.fit(GAMAsmVsize[,c('logmstar', 'rekpc')],
@@ -597,7 +600,7 @@ plot(fitGAMAsmVsizelogrenoerr, doellipse=FALSE, unlog='xy')
 
 ### Example using Tully-Fisher relation data ###
 
-\dontrun{
+\donttest{
 
 data(TFR)
 TFRfit=hyper.fit(X=TFR[,c('logv','M_K')],vars=TFR[,c('logv_err','M_K_err')]^2)
@@ -607,7 +610,7 @@ plot(TFRfit, xlim=c(1.7,2.5), ylim=c(-19,-26))
 
 ### Mase-Angular Momentum-Bulge/Total ###
 
-\dontrun{
+\donttest{
 
 data(MJB)
 MJBfit=hyper.fit(X=MJB[,c('logM','logj','B.T')], covarray=makecovarray3d(MJB$logM_err,

diff --git a/man/hyper.like.Rd b/man/hyper.like.Rd
@@ -47,7 +47,7 @@ Robotham, A.S.G., & Obreschkow, D., PASA, in press
 Aaron Robotham and Danail Obreschkow
 }
 \seealso{
-\code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
+\code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.fit-data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
 }
 \examples{
 #Setup the initial data:

diff --git a/man/hyper.plot.Rd b/man/hyper.plot.Rd
@@ -93,7 +93,7 @@ Robotham, A.S.G., & Obreschkow, D., PASA, in press
 Aaron Robotham and Danail Obreschkow
 }
 \seealso{
-  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
+  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.fit-data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
 }
 \examples{
 #### A very simple 2D example ####
@@ -189,7 +189,7 @@ plot(fitline, trans=0.2, asp=1)
 
 #### A 3D example with fitting a plane ####
 
-\dontrun{
+\donttest{
 
 #Setup the initial data:
 
@@ -258,7 +258,7 @@ plot(fitplane)
 
 #Full data
 
-\dontrun{
+\donttest{
 
 data(hogg)
 fithogg=hyper.fit(X=cbind(hogg$x, hogg$y), covarray=makecovarray2d(hogg$x_err, hogg$y_err,
@@ -288,7 +288,7 @@ hoggtrim$y_err, hoggtrim$corxy), fitobj=fithogg, trans=0.2, xlim=c(0, 300), ylim
 
 #### Example using 'real' data with intrinsic scatter ####
 
-\dontrun{
+\donttest{
 
 data(intrin)
 fitintrin=hyper.fit(X=cbind(intrin$x, intrin$y), vars=cbind(intrin$x_err,
@@ -302,7 +302,7 @@ plot(fitintrin, trans=0.1, pch='.', asp=1)
 
 #### Example using flaring trumpet data ####
 
-\dontrun{
+\donttest{
 
 data(trumpet)
 fittrumpet=hyper.fit(X=cbind(trumpet$x, trumpet$y), covarray=makecovarray2d(trumpet$x_err,
@@ -318,7 +318,7 @@ plot(fittrumpet, trans=0.1, pch='.', asp=1)
 
 #### Example using 6dFGS Fundamental Plane data ####
 
-\dontrun{
+\donttest{
 
 data(FP6dFGS)
 fitFP6dFGSw=hyper.fit(FP6dFGS[,c('logIe_J', 'logsigma', 'logRe_J')],
@@ -331,7 +331,7 @@ plot(fitFP6dFGSw, doellipse=FALSE, alpha=0.5)
 
 #### Example using GAMA mass-size relation data ####
 
-\dontrun{
+\donttest{
 
 data(GAMAsmVsize)
 fitGAMAsmVsize=hyper.fit(GAMAsmVsize[,c('logmstar', 'rekpc')],
@@ -360,7 +360,7 @@ plot(fitGAMAsmVsizelogrenoerr, doellipse=FALSE, unlog='xy')
 
 ### Example using Tully-Fisher relation data ###
 
-\dontrun{
+\donttest{
 
 data(TFR)
 TFRfit=hyper.fit(X=TFR[,c('logv','M_K')],vars=TFR[,c('logv_err','M_K_err')]^2)

diff --git a/man/hyper.sigcor.Rd b/man/hyper.sigcor.Rd
@@ -30,7 +30,7 @@
   Aaron Robotham and Danail Obreschkow
 }
 \seealso{
-  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
+  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.fit-data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
 }
 \examples{
 #The below will take *a long* time to run- of the order a few days for the LD tests.
@@ -41,12 +41,12 @@ testvec=c(5,10,20,50,100)
 
 set.seed(650)
 
-fittestmean={}
+convtest2dOpt={}
 for(Nsamp in testvec){
   print(paste('Nsamp=', Nsamp))
   fittest=matrix(0, nrow=Ngen, ncol=3)
   for(i in 1:Ngen){
-    if(i %% 100==0){print(paste('Ngen=',i))}
+    if(i \%\% 100==0){print(paste('Ngen=',i))}
     mockx=runif(Nsamp, -100, 100)
     mocky=mockx*tan(45*pi/180)+rnorm(Nsamp, sd=sdsamp)
     fittest[i,]=hyper.fit(X=cbind(mockx,mocky), coord.type='theta', scat.type='vert.axis')$parm
@@ -55,12 +55,12 @@ for(Nsamp in testvec){
   mean(fittest[,3])*hyper.sigcor(Nsamp, 2)))
 }
 
-fittestmeanLD={}
+convtest2dLD={}
 for(Nsamp in testvec){
   print(paste('Nsamp=', Nsamp))
   fittest=matrix(0, nrow=Ngen, ncol=3)
   for(i in 1:Ngen){
-    if(i %% 100==0){print(paste('Ngen=',i))}
+    if(i \%\% 100==0){print(paste('Ngen=',i))}
     mockx=runif(Nsamp, -100, 100)
     mocky=mockx*tan(45*pi/180)+rnorm(Nsamp, sd=sdsamp)
     fittest[i,]=hyper.fit(X=cbind(mockx,mocky), coord.type='theta', scat.type='vert.axis',
@@ -72,12 +72,12 @@ for(Nsamp in testvec){
   mean(fittest[,3])*hyper.sigcor(Nsamp, 2)))
 }
 
-normtestmean={}
+convtest1dNorm={}
 for(Nsamp in testvec){
   print(paste('Nsamp=', Nsamp))
   normtest={}
   for(i in 1:Ngen){
-    if(i %% 100==0){print(paste('Ngen=',i))}
+    if(i \%\% 100==0){print(paste('Ngen=',i))}
     normtemp=rnorm(Nsamp, sd=sdsamp)
     normtest=c(normtest, sqrt(sum((normtemp-mean(normtemp))^2)/Nsamp))
   }

diff --git a/man/hyper.summary.Rd b/man/hyper.summary.Rd
@@ -33,7 +33,7 @@ Prints out basic summary information for hyper.fit objects output by the hyper.f
   Aaron Robotham and Danail Obreschkow
 }
 \seealso{
-  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
+  \code{\link{hyper.basic}}, \code{\link{hyper.convert}}, \code{\link{hyper.fit-data}}, \code{\link{hyper.fit}}, \code{\link{hyper.plot}}, \code{\link{hyper.sigcor}}, \code{\link{hyper.summary}}
 }
 
 \examples{